Handling batch effects on cross-platform classification of microarray data
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Publication Details
Author list: Engchuan W., Meechai A., Tongsima S., Chan J.H.
Publisher: Inderscience
Publication year: 2016
Journal: International Journal of Advanced Intelligence Paradigms (1755-0386)
Volume number: 8
Issue number: 1
Start page: 59
End page: 76
Number of pages: 18
ISSN: 1755-0386
eISSN: 1755-0394
Languages: English-Great Britain (EN-GB)
Abstract
Gene-set-based microarray analysis is commonly applied in the classification of complex diseases. However, the robustness of a classifier is normally limited by the small number of samples in many microarray datasets. Although a merged dataset from multiple experiments may improve classification performance, batch effects or technical/biological variations among these experiments may eventually confound the analysis. Besides the batch effects, merging multiple microarray datasets from different platforms can generate missing values, due to a different number of covered genes. In this work, we extend previous works that focused on the missing value incident by further exploring the impact of batch effects on cross-platform classification. Two quality measures of data purity are proposed and two data imputation methods are compared. The results show that by doing batch correction the quality of the merged data is improved significantly. Furthermore, the classification performance is high when the normalised purity is above a certain threshold. Copyright ฉ 2016 Inderscience Enterprises Ltd.
Keywords
Batch effects correction, BPCA, ComBat, Complex diseases, Data imputation, Data purity, Gene set-based approach, kNN
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